Manual for Soil Analysis-Monitoring and Assessing Soil Bioremediation Phần 8 pptx

252 D.B. Hedrick et al. (Momchilova and Nikolova-Damyanova 2000), and special derivatization methods to determine the position and geometry of monounsaturation, such as MS of dimethyldisulfide adducts (Nichols et al. 1986). MS of picol- inyl esters provides more informative fragmentations than GC-MS of the methyl ester (Christie et al. 1991; Harvey 1992). This work presupposes some knowledge of Microsoft Excel (Microsoft Corp., Redmond, WA), which is used to manipulate chromatographic re - sults in man y laboratories. The on-line help system is the basic reference forExcel,suchasitis.Anoviceuserwillbenefitfromoneofthemanyintro- ductory books available at a bookstore. Also assumed is some background in the statistical procedures commonl y applied to PLFA data, including analysis of variance (ANOVA) and factor analysis. 12.2 Transforming Fatty Acid Peak Areas to Total Microbial Biomass Gas chromatography provides a peak area proportionalto the amountof the compound in the sample responsible for the peak. A known concentration of an internal standard, usually 19:0 or 21:0, is added to the sample before analysis to allow calculation of absolute amounts (see Sect. 12.5 for the naming of fatty acids). The equation used to calculate the total amount of fatty acids in a sample is, FA = (sum A FA /A IS ) × IS × X Y (12.1) FA totalpicomoles of fatty acids per gram dry mass ofsample (pmol/g dry mass) sum A FA sum of the areas of all identified fatty acid peaks excluding the internal standard A IS area of the int ernal standard peak IS concentration of internal standard used (50 pmole /µL) X volume of internal standard used to dilute the fatty acid methyl esters ( µL) Y mass of sample extracted (g soil dry mass). In some instances, rather than grams dry mass as the divisor, it will be volume of water (L), surface area in meters squared, or some other extensive variable. 12 Interpretation of Fatty Acid Profiles of Soil Microorganisms 253 Many analysts calculate the pmol/g dry mass for each fatty acid, then add them together to get the total pmole /g dry mass. This is not good practice, since the pmol/g dry mass for each fatty acid is not then of use in further analysis, and the more complicated calculation makes more work and op portunities for error. The total moles of membrane fa tty acids is proportional to the total microbial biomass. The constant of proportionality used in our laboratory is 2.5 × 10 4 cells/pmol PLFA (Balkwill et al. 1988; White et al. 1996 and references therein). This con v ersion factor was derived from measurements on laboratory cultures, so the number of cells will be underestimated for environments populated by smaller bacterial cells, such as oligotrophic environments. Researchers who cou n t cells, with automated cell cou n ting instruments or by microscopy, are often uncomfortable with measurements of viable biomass expressed as moles of PLFA or grams dry mass of cells. In order to estimate cell counts from moles of PLFA requires knowledge of the distribution of cell sizes in the sample and the amount of PLFA per cell for differen t sizes, information which is not usually available. It makes more sense to transform cell counts to moles PLFA or from the latter to grams dry weight of cells, since the cell counting can provide the data on cell size distribution. Fo r most sample sets, the biomass will not be normally distributed, that is, a histogram of the biomass data will be skewed with a long tail toward the higher biomasses. This can be tested for by using the standard f-test for normality. Also, in most biomass data sets, the variance of biomass increases with the absolute value of the biomass. This violates the assump- tions of parametric statistics, including ANOVA and factor analysis, and lowers the power of any statistical test employed. These problems can be solved by a log(X+A)transformation,whereXisthemolepercentofthe fatty acid, and A is a small constant. The small constant is added so that zero values give a real solution when the log transform is applied. The most commo n value used for A is one, which gives a value of zero for the transform when X is zero, since log(0 + 1) = 0. There are two approaches to proving the value of applying a log trans- form to biomass data, the theoretical and the practical. The theoretical explanation involves the scaling of the forces affecting microbial biomass (Magurran 1988) and thefractal structureof microbial environments(Man- delbrot 1982), and is beyond the scope of this work. The practical reason for the log transform is that it works; applying a log transformation to t he data is perfectly legitimate, and results in more significant differences on statistical tests. 254 D.B. Hedrick et al. 12.3 Calculation and Interpretation of Community Structure After the biomass, the next most important information to extract from aPLFAprofileisthecommunitystructure.Butwherethebiomassisasingle value for each sample with a straightforward in terpretation, the commu- nity structure data is multivariate with many options in its interpretation. A “standard” method for presenting comm unity structure da ta, how to create a custom method for community structure, and factor analysis will be presented. 12.3.1 Standard Community Structure Method In the standard method for community structure anal ysis of PLFA pro- files, chemically related fatty acids are grouped as in Table 12.1. A PLFA profile may contain, for example, from 18 to 92 fatty acids. The standard community structure approach summarizes that in six variables, which are justthesumofthemolepercentsofeachofthefattyacidgroups.Theuse of a standard community structure analysis method allows comparison between/among experiments. Table 12.1.Groupsofchemically relatedfattyacids usedin the standardcommunity structure analysis Group name R ule Examples Microbiota represented Saturates Saturated straight- chain fa tty acids 12:0, 13:0, 14:0, 15:0, 16:0, 17:0, 18:0 All organisms Monounsaturates Fatty acids with a single unsaturation plus cyclopropyls 14:1 ω5c, 16:1 ω7c, 16:1 ω7t, 18:1ω7c Proteobacteria Mid-chain branched Any mid-chain branched fatty acid 10Me16:0, 10Me18:0 Actinomycetes, sulfate-reducers Terminally branched Iso-andanti-iso- branched saturated fatty acids i14:0, i15:0, a15:0, i16:0, i17:0, a17:0 Gram positive bacteria Po lyunsaturates Any fatty acid with more than one unsaturation 18:2 ω6c, 18:3 ω3c Eukaryotes Branched unsaturates Any branched mono unsaturate i17:1ω7c Anaerobes 12 Interpretation of Fatty Acid Profiles of Soil Microorganisms 255 The standard community structure breakdown was originally devel- oped on marine sediments, and has been successfully applied to microbial communities from many environments, including, for example, marine macrofaunal burrows (Marinelli et al. 2002), a subsurface zero-valent iron reactive barrier for bioremediation (Gu et al. 2002), marine gas hydrates (Zhang et al. 2002), soils contaminated with jet fuel (Stephen et al. 1999), and to a comparison of subsurface environments (Kieft et al. 1997). 12.3.2 Custom Community Structure Methods When examination of the chromatograms or the mole percent table shows differ ences with treatment, but no significant differences are found in the standardcommunity structuregroups, some otherwayofgroupingthefatty acids may be more useful. For example, if samples differ in the proportions of Cyanobacteria and Eukaryotic algae, it may be useful to separate the polyunsaturates with 18 or fewer carbons characteristic of Cyanobacteria (Øezanka et al. 2003) from those typical of Eukaryotic algae with 20 or more carbons (Erwin 1973). There are several methods for developing alternative community struc- ture groups. The manual method uses the pattern recognition power of the human eye. The PLFA chromatograms are printed on the same scale and spread out on a large table. Similar-looking chromatograms are grouped together and different-looking ones are placed in separate groups. While very low-tech, this works remarkably well. This same approach can be ap- plied to a mole percent table by printing it out, cutting out a strip for each sample, and sorting the samples by similarity. Once the samples have been sorted into similar groups, the fatty acids responsible are summed to form new community structure groups. Given access to statistical software, a triangular table of Pearson’s r correlation coefficients is usually available as an output option. Visual examinationof this table will locate fatty acids with high co rrelations, which are then grouped together to form new community structure groups. 12.3.3 Factor Analysis Factor analysis incl udes several relat ed methods, including principal-com- ponents analysis. The virtue of this method is that it automatically con- structs fatty acid groups reflecting the differences in community structure, rather than applying a preconception of fatty acid groups. The data deter- mines the fatty acid groups, rather than the analyst. Factor loadings greater 256 D.B. Hedrick et al. than 0.7 indicate fatty acids with “significant” effects on the results. The factor scores are new variables that are linear combinations of the origi- nal values. These new variables can be submitt ed to statistical tests such as ANOVA like any other variable. Examples of the application of factor analysis to PLFA profiles include storage perturbation of soil micro bial communities (Haldeman et al. 1995; Brockman et al. 1997), soils at differ - ent temperatures (Zogg et al. 1997), and soils from different ecosystems (Myers et al. 2001). The results of factor analysisareusuallyimproved byapplying the log(X+ 1) transformation to the mole percent data before factor analysis. A rough method to determine whether themole perc ent data is normally distributed is to calculate the maximum, average, and the minimum not equal to zero for each fatty acid. The formulas for these in Excel are “ = max(b2.b45)”, “= average(b2.b45)”, and “= min(if(b2.b45 = 0, 100, b2.b45))”, where b2.b45 is the range containing the data. The formula for min 0 is what Excel terms an array formula; you have to hold down the Shift and Control keys while you press Enter to enter the formula. If the difference between the maximum and average is greater than the difference between the average and the minimum 0 for most of the fatty acids, then the data is not normally distributed and the log(X+1) transformation will probably improve results. There are theoretical reasons to advocate the arcsin[square root(X)] transformation over the log(X+1) transformation, but very little difference is found in practice, and the log(X + 1) is simpler to apply and explain. Similarly, there are theoret ical reasons to prefer factor analysis sensu stricto over principal components analysis, and vice versa, which can, and have been, argued for days to no conclusion. In practice, the two methods give very similar results. 12.4 Calculation and Interpretation of Metabolic Stress Biomarkers The membrane of the bacterial cell handles all of its interactions with its environment, and bacteria have many strategies to deal with stressful environmental conditions, incl uding modifying the fa tty acids used in the membrane. This is illustrated in Eq. (12.2), where S stands for the substrate fatty acid and P for the product fatty acid induced by metabolic stress, namely, a trans monounsaturate or cyclopropyl fatty acid. S → P cis monounsaturate → trans monounsaturate cis monounsaturate → cyclopropyl (12.2) 12 Interpretation of Fatty Acid Profiles of Soil Microorganisms 257 The stress biomarkers are then calculated as the ratio of the mole percents of the product to the substrate fatty acids, as in Eq. (12.3): BM Stress = P/S (12.3) where BM Stress is the value of the stress biomarker. The mostcommon trans- formations are 16:1 ω7c→16:1ω7 t, 16:1ω7c→Cy17:0, 18:1ω7c→18:1ω7t, and 18:1 ω7c→Cy19:0. There are problems with the application of the stress biomarkers. The first type of problem is when the stress-induced product fatty acid is only detected in a minority ofthesamples. This will most likelyprevent detection of statistically significant differences. The second problem is when the substrate fatty acid is not detected, but the stress-induced fatty acid is; this has been seen in hot acid environments such as hydrothermal systems. Since division by zero is undefined in standard algebra, undefined results appear that standard statistical programs are unable to use. This problem can be solved by a modification of Eq. (12.3), BM Stress = P/(S + 1) (12.4) The metabolic stress biomarkers have been applied to, for example, tap water biofilms (White et al. 1999), and soils contaminated with jet fuel (Stephen et al. 1999). 12.5 Naming of Fatty Acids Creating clear,consistent, and unambiguous names formicrobial fatty acids is challenging due to the wide variety of possible structures. At the same time, it is essential for understanding the data and communicating results. The IUPAC rules for naming chemical compounds are supposed to provide unambiguous names, but there are problems with this approach. The most important is that IUPAC counts carbons from the opposite end of the fatty acid molecule from most of the enzymes that modify the fatty acid. The need for a compact notation has led to the development of the omega system for naming fatty acids. Fatty acids are named according to the pattern of A:B ωC. The A stands for the number of carbon atoms in the fatty acid backbone, B is the number of double bonds, and C is distance of the nearest unsaturation from the aliphatic ( ω)endofthemolecule. Thiscanbefollowedbya“c”forcisora“t”fortransconfigurationof the unsaturation. The prefixes “i,” “a,” and “br” stand for iso, anti-iso, and unknown branching position of the carbon chain, respectively. Mid- chain branching is noted by a prefix “10M e” for a 10-methyl fatty acid, and 258 D.B. Hedrick et al. cyclopropyl fatty acids by prefix “Cy.” For example: 18:1ω7c is 18 carbons long with one double bond occurring at the 7th carbon a tom from the ω end, and the unsa turation is in the cis conformation. Also, 16:0, i16:0, a16:0, and br16:0 are all 16-carbon fatty acids, while 10Me16:0 and Cy17:0 both contain a total of 17 carbons, not counting the carbon of the methyl ester moiety. References Balkwill DL, Leach FR, Wilson JT, McNabb JF, White DC (1988) Equivalence of micro- bial biomass measures based on membrane lipid and cell wall components, adenosine triphosphate, and direct counts in subsurface sediments. Microbial Ecol 16:73–84 Brockman FJ, Li SW, Fredrickson JK, Ringelberg DB, Kieft TL, Spadoni CS, White DC, McKinley JP (1997) Post-sampling changes in microbial community com position and activity in a subsurface paleosol. Microbial Ecol 36:152–164 Christie WW (2003) Lipid analysis; isolation, separation, identification and structural anal- ysis of lipids, 3rd edn. Oily Press, Bridgwater, UK Christie WW, Brechany EY, Lie Ken Jie MSF, BakareO (1991) MS characterizationof pico linyl and methyl ester derivatives of isomeric thia fatty acids. Biol Mass Spectrom 20:629–635 Erwin JA (1973) Fatty acids in eukaryotic microorganisms. In: Erwin JA (ed) Lipids and biomembranes of eukaryotic microorganisms. N ew York, Academic Press, pp 41–143 Griffin WT, Phelps TJ, Colwell FS, Fredrickson JK (1997) Methods for obtaining deep subsurface microbiological samples by drilling. In: Amy PS and Haldeman DL (eds) The microbiology of the terrestrial and deep subsurface. CRC Press, Boca Raton, pp 23– 43 Grob RL Barry EF (1995) Modern practice of gas chromatography. Wiley, New York Gu B, Zhou J-Z, Watson DB, Philips DH, Wu L, White DC (2001) Microbiological character- ization in a zero-valent iron reactive barrier. Appl Environ Microbiol 77:293–309 Haldeman DL, Amy PS, Ringelberg DB, White DC (1994) Changes in bacteria recoverable from subsurface volcanic rock samples during storage at 4 ◦ C. Appl Enviro n Microbiol 60:2679–2703 Harvey DJ (1992) Mass spectrometry of picolinyl and other nitrogen-containing derivatives of fatty acids. In: Christie WW (ed) Advances in lipid methodology, vol 1. Oily Press, Ayr, UK, pp 19–80 Kieft TL, Murphy EM, Amy PS, Haldeman DL, Ringelberg DB, White DC (1997) Laboratory and field evidence for long-term starvation survival of microorganisms in subsurface terrestrial environments. In: Proceed instruments, methods, and missions for the in- vestigation of extraterrestrial organisms, 27 July to 1 August. Int Soc Optical Engin, San Diego, CA Magurran AE (1988) Chapt. 2. Diversity indices and species abundance models. In: Magur- ran AE (ed) Ecological diversity and its measurement. Princeton Univ Press, Princeton, NJ Mandelbrot B (1982) The fractal geometry of nature. Freeman, San Francisco, CA Marinelli RL, Lovell CR, Wakeham SG, Ringelberg D, White DC (2000) An experimental in- vestigation of the control of bacterial community composition in macrofaunal burrows. Marine Ecol Prog Series 235:1–13 Momchilova S, Nikolova-Damyanova B (2003) Stationary phases for silver ion chromatog- raphy of lipids: Preparation and properties. J Sep Sci 26:261–270 12 Interpretation of Fatty Acid Profiles of Soil Microorganisms 259 Myers RT, Zak DR, Peacock A, White DC (2001) Landscape-level patterns of microbial community composition and substrate. use in forest ecosystems. Soil Sci Soc Am J 65:359–367 Nichols PD, Guckert JB, White DC (1986) Determination of monounsaturated double bond position and geometry for microbial monocultures and complex consortia by capillary GC-MS of their dimethyl disulphide adducts. J Microbiol Meth 5:49–55 Phelps TJ, Fliermans CB, Garland TR, Pfiffner SM, White DC (1989) Recovery of deep subsurface sediments for microbiological studies. J Microbiol Meth 9:267–280 Øezanka T, Dor I, Prell A, Dembitsky VM (2003) Fatty acid composition of six freshwater wild cyanobacterial species. Folia Microbiol 48:71–75 Stephen JR, Chang Y-J, Gan YD, Peacock A, Pfiffner SM, Barcelona MJ, White DC, Mac- naughton SJ (1999) Microbial characterization of JP-4 fuel contaminated-site using a combined lipid biomarker/PCR-DGGE based approach. Environ Microbiol 1:231–241 White DC, Kirkegaard RD, Palmer Jr. RJ, Flemming CA, Chen G, Leung KT, Phiefer CB, Arrage AA (1999) The biofilm ecology of microbial biof ouling, biocide resistance and corrosion. In: Keevil CW, Godfree A, Holt D, Dow C (eds) Biofilms in the aquatic environment. Roy Soc Chem, Cambridge, UK, pp 120–130 White DC, Pinkart HC, Ringelberg DB (1996) Biomass measurements: biochemical ap- proaches. In: Hurst CH, Knudsen GR, McInerney MJ, Stetzenbach LD, Walter MV (eds) Manual of environmental microbiology, 1st ed. AS M Press, Washington, DC, pp 91–101 Zhang CL, Li Y, Wall JD, Larsen L, Sassen R, Huang Y, Wang Y,Peacock A, White DC, Ho rita J, Cole DR (2001) Lipid and carbon isotopic evidence of methane-oxidizing and sulfate- reducing bacteria in association with gas hydrates from the Gulf of Mexico. Geology 30:239–242 Zogg GP, Zak DR, Ringelberg DB, MacDonald NW, Pregitzer KS, White DC (1997) Compo- sitional and functional shifts in micro bial communities related to soil warming. Soil Sci Soc Amer J 61:475–481 13 Enumeration of Soil Microorganisms Julia Foght, Jackie Aislabie 13.1 Sample Preparation and Dilution ■ Introduction Objectives. Soil is a heterogeneous matrix in which microbes are associated with organic and inorganic soil particles, forming aggregates. The goals of sample preparation for conventional enumeration techniques are to release the microbes from the matrix of a representative soil sample, then disperse them in a suitable diluent so that individual cells can be enumerated ei- ther by microscopic visualization or cultivation methods. The basic meth- ods for soil aggregate disruption and dilution have been in common use for decades, but individual laboratories often develop variations to create their own empirical “standard methods.” Different soil types may be more amenable to certain diluents or disruption techniques, so, if examining an unfamiliar soil type, it is wise to test combinations of methods to empir- ically optimize enumera tion results. The presence of inorganic or organic contaminants (e.g., crude oil) may require adaptation of the basic methods todispersethesoilsampleadequatelyordiluteatoxicant(e.g.,heavymetal). Principle. A suitable buffered diluent releases microbial cells from the soil matrix and is used to dilute the suspension to a cell density suitable for the enumeration method to be used. The dilution method must not compro- misethestructuralintegrityofcellstobeenumeratedbymicroscopy,nor the viability of cells for culture-based enumeration. Theory . Microbes in soil are distributed heterogeneously in microenviron- men ts of diff erent scales and along depth pr ofiles (Foster 1988; Ranjard and Richaume 2001). Therefore, representa tive samples of a suitable size must be collected for accurate enumeration. The number of individual samples theoretically required to represent the site can be calculated (Alef and Nan- nipieri 1995), but in practical terms the number of samples handled is Julia Foght: Biological Sciences, University of Alberta, Edmon ton AB, Canada T6G 2E9, E-mail: julia.foght@ualberta.ca Jackie Aislabie: Landcare Research, Private Bag 3127, Hamilton, New Zealand Soil Biology, Volume 5 Manual for Soil Analysis R. Margesin, F. Schinner (Eds.) c  Springer-Verlag Berlin Heidelb erg 2005 262 J. Foght, J. Aislabie dictatedbythetimeandresourcesavailable.Asacompromise,acomposite sample can be prepared from several samples of equal mass or volume, but statistical evaluation of the data is relinquished. Commonly, at least 10g wet mass of soil is used to prepare the first dilution, although the sample size maybeadjustedaccordingtothesoiltypeandtheorganismstobeenumer- ated. S erial dilutions (commonly ten-fold) of soil suspensions are prepared with sufficient mixing to disrupt soil aggregates and release occluded mi- crobes into suspension. Physical disruption of the soil aggregates can be enhanced by inclusion of small (2−3 mm) sterile glass beads in the diluent, at least in the first dilution. Suitable sterile diluents, of which many exist, aid the dispersion of soil aggregates. Diluents are often buffered (Strick- land et al. 1988) and may contain proteins such as gelatin or tryptone to aid dispersion, glycerol to aid resuscitation of starved bacterial cells (Trevors and Cook 1992), or a surface active agent such as 0.1% Tween 80, although surfactantsmay reduce counts of sensitive Gram-negativecells (Koch 1994). ■ Equipment • Top-loading balance capable of weighing to 0.1 g • 150-mL glass dilution bottles and, optionally, approx. 20 g of 2−3 mm glass beads per bottle to aid in disruption of soil aggregates • Spatula or small spoon, sterilized by autoclave or by flaming with ethanol • Sterile pipettes for serial dilutions: 10-mL wide-mouth glass pipettes are less likely to plug during initial dilutions • Optional mixing equipment: reciprocating or gyratory shaker for first dilution;vortexmixer;Waringblender ■ Reagents • Suitable sterile, buffered diluent dispensed into dilution bottles, usually 90 or 99 mL each • Suitable diluents include: 0.1% (w/v) sodium pyrophosphate with or without 1% glycerol (Trevors and Cook 1992); phosphate-buffered saline (0.85% (w/v) NaCl,2.2mM KH 2 PO 4 ;4.2mM Na 2 HPO 4 ,pH7)withor withou t 0.01% gelatin or peptone (Koch 1994); 1−10 mM potassium phosphate (pH 7); or mineral salts medium lacking carbon source (Atlas 1995). ■ Sample Collection Acceptable aseptic techniques for collection and storage of soil samples are giveninChapt. 1 inthisvolume.Soil intended forconventionalenumeration [...]... Strickland TC, Sollins P, Schimel DS, Kerle EA (1 988 ) Aggregation and aggregate stability in forest and range soils Soil Sci Soc Am J 52 :82 9 83 3 Trevors JT, Cook S (1992) A comparison of plating media and diluents for enumeration of aerobic bacteria in a loam soil J Microbiol Meth 14:271–275 Van Elsas JD, Smalla K, Lilley AK, Bailey MJ (2002) Methods for sampling soil microbes In: Hurst CT, Crawford... Microbiol 68: 387 8– 388 5 Créach V, Baudoux A-C, Betru G, Le Rousiz B (2003) Direct estimate of active bacteria: CTC use and limitations J Microbiol Meth 52:19– 28 Davis KER, Joseph SJ, Janssen PH (2005) Effects of growth medium, inoculum size, and incubation time on the culturability and isolation of soil bacteria Appl Environ Microbiol 71 :82 6 83 4 Eaton AD, Clesceri LS, Greenberg AE (1995) Standard methods for. .. of soils developed under very different environmental conditions, especially in contaminated and remediated soils Principle Soils are fumigated with chloroform, incubated for 24 h, and extracted Different components can then be measured in the extracts, using various methods (Sects 14.3–14.4) Non-fumigated soil is also extracted to correct for non-biomass soil organic matter Theory Following chloroform... samples and should be subtracted from sample counts before calculation of total numbers I Calculation Counts are calculated on the basis of wet mass of soil, corrected for background, and usually expressed on the basis of dry mass of soil – Cells/g soil wet mass = 1 total no of cells counted total stained area × × total no of FOV area of FOV mass of soil on filter – Cells/g soil dry mass = (cells/g soil. .. tubes with 1 g of soil in 9 mL of diluent and mixing by vortex, but caution should be used because small sample sizes may not be representative • A sonicator bath or probe may be used for initial soil sample disruption (Strickland et al 1 988 ), but this equipment is not standard in all laboratories, and excess sonication will reduce counts • Aggregates in hydrocarbon-contaminated soils may be difficult... methodological review and recommendations for applications in ecological research Biol Fertil Soils 36:249–259 Bottomley PJ (1994) Light microscopic methods for studying soil microorganisms In: Weaver RW, Angle S, Bottomley P, Bezdicek D, Smith S, Tabatabai A, Wollum A (eds) Methods of soil analysis, Part 2, Microbiological and biochemical properties Soil Sci Soc Am Book Series No 5, Madison WI, pp 81 –105 Brown... 68: 2 683 –2 689 Joseph SJ, Hugenholtz P, Sangwan P, Osborne CA, Janssen PH (2003) Laboratory cultivation of widespread and previously uncultured soil bacteria Appl Environ Microbiol 69:7210– 7215 Kepner RL, Pratt JR (1994) Use of fluorochromes for direct enumeration of total bacteria in environmental samples – past and present Microbiol Rev 58: 603–615 Kirchman D, Sigda J, Kapuscinski R, Mitchell R (1 982 )... Organic C (Vance et al 1 987 ), total N and NH4 -N (Brookes et al 1 985 ), and ninhydrin-reactive N (Joergensen and Brookes 1990) can be measured in the same 0.5 M K2 SO4 extract (Alef and Nannipieri 1995) Organic C (Joergensen 1995) and total S (Wu et al 1993) can be measured after extraction with 0.01 M CaCl2 and phosphate or total P after extraction with NaHCO3 (Brookes et al 1 982 ) I Equipment • Room,... containing 10 mM KH2 PO4 , 0 .85 % NaCl and 5 mM MgCl2 · 6H2 O • Non-fluorescent immersion oil I Sample Preparation Prepare suitable dilutions of soil sample (Sect 13.1) in sterile, particle-free diluent I Procedure 1 Prepare dilution series as required in filter-sterilized diluent Vigorously mix sample for 5 min and allow suspension to stand for approx 30 min to let larger soil particles settle out If... examination of water and wastewater, 19th Edition, American Public Health Association, Washington DC, Section 9221 Efroymson RA, Alexander M (1991) Biodegradation by an Arthrobacter species of hydrocarbons partitioned into an organic solvent Appl Environ Microbiol 57:1441–1447 Foster RC (1 988 ) Microenvironments of soil microorganisms Biol Fertil Soils 6: 189 –203 Fry JC (1990) Direct methods and biomass estimation . a log trans- form to biomass data, the theoretical and the practical. The theoretical explanation involves the scaling of the forces affecting microbial biomass (Magurran 1 988 ) and thefractal. environments(Man- delbrot 1 982 ), and is beyond the scope of this work. The practical reason for the log transform is that it works; applying a log transformation to t he data is perfectly legitimate, and results. and “br” stand for iso, anti-iso, and unknown branching position of the carbon chain, respectively. Mid- chain branching is noted by a prefix “10M e” for a 10-methyl fatty acid, and 2 58 D.B. Hedrick